Improving the time-machine: estimating date of birth of grade II gliomas
Objectives: Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase line...
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Published in | Cell proliferation Vol. 45; no. 1; pp. 76 - 90 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
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Oxford, UK
Blackwell Publishing Ltd
01.02.2012
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Abstract | Objectives: Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity.
Materials and methods: We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters.
Results and conclusions: We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours. |
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AbstractList | Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion-proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity.
We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters.
We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours. Objectives: Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. Materials and methods: We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. Results and conclusions: We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours. OBJECTIVESHere we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion-proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. MATERIALS AND METHODSWe have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. RESULTS AND CONCLUSIONSWe show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours. Abstract Objectives: Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. Materials and methods: We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. Results and conclusions: We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/ v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours. Objectives: Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. Materials and methods: We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. Results and conclusions: We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/ v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours. |
Author | Deroulers, C. Grammaticos, B. Pallud, J. Badoual, M. Varlet, P. Taillandier, L. Mandonnet, E. Duffau, H. Bauchet, L. Capelle, L. Gerin, C. |
AuthorAffiliation | 3 Department of Neuropathology, Sainte‐Anne Hospital, Paris, France 7 Department of Neurosurgery, Pitié‐Salpétrière Hospital, Paris, France 2 Department of Neurosurgery, Sainte‐Anne Hospital, Paris, France 4 Department of Neurosurgery, Lariboisière Hospital, Paris, France 5 Department of Neuro‐Oncology, Nancy Neurological Hospital, Nancy, France 8 Réseau d’Etude des Gliomes, REG, Groland, France 1 IMNC Laboratory, Paris VII‐Paris XI Universities, CNRS, UMR 8165, Orsay, France 6 Department of Neurosurgery, CHU of Montpellier, Montpellier, France |
AuthorAffiliation_xml | – name: 4 Department of Neurosurgery, Lariboisière Hospital, Paris, France – name: 6 Department of Neurosurgery, CHU of Montpellier, Montpellier, France – name: 1 IMNC Laboratory, Paris VII‐Paris XI Universities, CNRS, UMR 8165, Orsay, France – name: 8 Réseau d’Etude des Gliomes, REG, Groland, France – name: 2 Department of Neurosurgery, Sainte‐Anne Hospital, Paris, France – name: 5 Department of Neuro‐Oncology, Nancy Neurological Hospital, Nancy, France – name: 7 Department of Neurosurgery, Pitié‐Salpétrière Hospital, Paris, France – name: 3 Department of Neuropathology, Sainte‐Anne Hospital, Paris, France |
Author_xml | – sequence: 1 givenname: C. surname: Gerin fullname: Gerin, C. organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France – sequence: 2 givenname: J. surname: Pallud fullname: Pallud, J. organization: Department of Neurosurgery, Sainte-Anne Hospital, Paris, France – sequence: 3 givenname: B. surname: Grammaticos fullname: Grammaticos, B. organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France – sequence: 4 givenname: E. surname: Mandonnet fullname: Mandonnet, E. organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France – sequence: 5 givenname: C. surname: Deroulers fullname: Deroulers, C. organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France – sequence: 6 givenname: P. surname: Varlet fullname: Varlet, P. organization: Department of Neuropathology, Sainte-Anne Hospital, Paris, France – sequence: 7 givenname: L. surname: Capelle fullname: Capelle, L. organization: Department of Neurosurgery, Pitié-Salpétrière Hospital, Paris, France – sequence: 8 givenname: L. surname: Taillandier fullname: Taillandier, L. organization: Department of Neuro-Oncology, Nancy Neurological Hospital, Nancy, France – sequence: 9 givenname: L. surname: Bauchet fullname: Bauchet, L. organization: Department of Neurosurgery, CHU of Montpellier, Montpellier, France – sequence: 10 givenname: H. surname: Duffau fullname: Duffau, H. organization: Department of Neurosurgery, CHU of Montpellier, Montpellier, France – sequence: 11 givenname: M. surname: Badoual fullname: Badoual, M. organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France |
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Snippet | Objectives: Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential... Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation... Abstract Objectives: Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a... OBJECTIVESHere we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential... Objectives: Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential... |
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SubjectTerms | Cell Proliferation Glioma - pathology Humans Models, Biological Models, Statistical Neoplasm Grading Original Time Factors |
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Title | Improving the time-machine: estimating date of birth of grade II gliomas |
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